Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

Wang, Lei; Xu, Xin; Hui, Dong; Gui, Rong; Pu, Fangling

doi:10.3390/s18030769

Cited by 40 publications

(27 citation statements)

References 33 publications

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“…To objectively evaluate the effectiveness of the proposed method, our algorithm is compared against three state-of-the-art algorithms. They include RV-CAE, WAE, WCAE, and fixed-feature-size CNN (FFS-CNN) [41]. To ensure the fairness of comparison, firstly, the input information content of RV-CAE should be equivalent with that of CV-CAE, so the input elements of RV-CAE are designed as {C 11 , C 22 , C 33 , real(C 12 ), imag(C 12 ), real(C 13 ), imag(C 13 ), real(C 23 ), imag(C 23 )}.…”

Section: Comparative Algorithmsmentioning

confidence: 99%

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

et al. 2019

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Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consider the amplitude information of the pixels in PolSAR images failing to obtain adequate discriminative features. In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted. Benefiting from the advantages of the CAE, CV-CAE extract features from a tiny number of training datasets. To further boost the performance, we propose a novel post processing method called spatial pixel-squares refinement (SPF) for preliminary classification map. Specifically, the majority voting and difference-value methods are utilized to determine whether the pixel-squares (PixS) needs to be refined or not. Based on the blocky structure of land cover of PolSAR images, SPF refines the PixS simultaneously. Therefore, it is more productive than current methods worked on pixel level. The proposed algorithm is measured on three typical PolSAR datasets, and better or comparable accuracy is obtained compared with other state-of-the-art methods.

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Section: Comparative Algorithmsmentioning

confidence: 99%

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

et al. 2019

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“…A lot of PolSAR image classification methods have been developed based on CNNs (Wang et al, 2018a;Zhou et al, 2017;De et al, 2018a;Guo et al, 2017;Bi et al, 2019). The aim of PolSAR image classification is to give a certain category to every pixel, which can be seen as a semantic segmentation problem in computer vision.…”

Section: Cnns For Polsar Classificationmentioning

confidence: 99%

Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework

Zhang

Dong

Zou

2019

ISPRS Journal of Photogrammetry and Remote Sensing

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Convolutional neural networks (CNNs) have been widely used to improve the accuracy of polarimetric synthetic aperture radar (PolSAR) image classification. However, in most studies, the difference between PolSAR images and optical images is rarely considered. Most of the existing CNNs are not tailored for the task of PolSAR image classification, in which complex-valued PolSAR data have been simply equated to real-valued data to fit the optical image processing architectures and avoid complex-valued operations. This is one of the reasons CNNs unable to perform their full capabilities in PolSAR classification. To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form. In this paper, considering the properties of complexvalued numbers, amplitude and phase of complex-valued PolSAR data are extracted as the input for the first time to maintain the integrity of original information while avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN) architecture is proposed to match the improved input form and achieve better classification results. Furthermore, depthwise separable convolution is introduced to the proposed architecture in order to better extract information from the phase information. Experiments on three PolSAR benchmark datasets not only prove that using amplitude and phase as the input do contribute to the improvement of PolSAR classifica-

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“…Dong et al [27] utilized spatial polarization information and XGBoost to perform classification experiments on the PolSAR images of the Gaofen-3 satellite, and proved that the combination of spatial information helps improve the overall performance. Wang et al [28] proposed a fixed-feature-size CNN, which realized the classification of all pixels of a PolSAR image at the same time, and improved the classification accuracy by using the correlation between different land covers. Shao et al [29] effectively reduced the impact of data imbalance on SAR image recognition results by introducing a visual attention mechanism and a new weighted distance measure loss function into the designed network.…”

Section: Introductionmentioning

confidence: 99%

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Ying

Chen

Zhai

et al. 2020

Sensors

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Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model's recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

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Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

Cited by 40 publications

References 33 publications

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

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